Prediction of mechanical properties of rolled steel based on dual-attention multiscale convolutional neural network

Abstract

The mechanical properties of carbon steel sheet directly depend on the chemical composition and process parameters of the steel. There is a complex nonlinear relationship between chemical composition, process parameters, and mechanical properties, and the establishment of a model with the ability to automatically distinguish the importance of different parameters, and to have good prediction accuracy and generalisation in different data sets is one of the important issues in the field of carbon steel mechanical properties prediction. In this paper, we proposed a multi-scale convolutional network model based on dual-attention mechanism (DAM-MSDSC), where the dual-attention module mines from features and channels at the same time, making the model focus on the important features. The multiscale feature fusion module effectively improves the model prediction accuracy by mining features from the output of the middle layer of the network to obtain features containing multiscale fusion features. In hot-rolled steel, for the two parameters of carbon content and temperature, it is confirmed that the change of carbon content has a greater impact on TS, while FTT and CT have a greater impact on YS. Through comparative experiments on different datasets, the proposed model had the best prediction performance, in which the correct rates of YS, TS, and EL are 98 %, 98.7 %, and 99.4 %, respectively, in cold rolled steel, and 97.8 %, 98.6 %, and 99.2 %, respectively, in hot rolled steel.